import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV

german_credit=pd.read_csv('GermanCredit.csv')

german_credit.head()

	Duration	Amount	InstallmentRatePercentage	ResidenceDuration	Age	NumberExistingCredits	NumberPeopleMaintenance	Telephone	ForeignWorker	Class	...	OtherInstallmentPlans.Bank	OtherInstallmentPlans.Stores	OtherInstallmentPlans.None	Housing.Rent	Housing.Own	Housing.ForFree	Job.UnemployedUnskilled	Job.UnskilledResident	Job.SkilledEmployee	Job.Management.SelfEmp.HighlyQualified
0	6	1169	4	4	67	2	1	0	1	Good	...	0	0	1	0	1	0	0	0	1	0
1	48	5951	2	2	22	1	1	1	1	Bad	...	0	0	1	0	1	0	0	0	1	0
2	12	2096	2	3	49	1	2	1	1	Good	...	0	0	1	0	1	0	0	1	0	0
3	42	7882	2	4	45	1	2	1	1	Good	...	0	0	1	0	0	1	0	0	1	0
4	24	4870	3	4	53	2	2	1	1	Bad	...	0	0	1	0	0	1	0	0	1	0

5 rows × 62 columns

colNames=german_credit.columns

#reclassify 'class' variable, 0='Bad',1='Good'
german_credit['Class']=german_credit['Class'].replace('Good',1).replace('Bad',0)

german_credit.head(3)

	Duration	Amount	InstallmentRatePercentage	ResidenceDuration	Age	NumberExistingCredits	NumberPeopleMaintenance	Telephone	ForeignWorker	Class	...	OtherInstallmentPlans.Bank	OtherInstallmentPlans.Stores	OtherInstallmentPlans.None	Housing.Rent	Housing.Own	Housing.ForFree	Job.UnemployedUnskilled	Job.UnskilledResident	Job.SkilledEmployee	Job.Management.SelfEmp.HighlyQualified
0	6	1169	4	4	67	2	1	0	1	1	...	0	0	1	0	1	0	0	0	1	0
1	48	5951	2	2	22	1	1	1	1	0	...	0	0	1	0	1	0	0	0	1	0
2	12	2096	2	3	49	1	2	1	1	1	...	0	0	1	0	1	0	0	1	0	0

3 rows × 62 columns

X=german_credit.drop('Class',axis=1)
y=german_credit.Class

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3,random_state=3)

clf = DecisionTreeClassifier(max_depth=2)

clf.fit(X_train,y_train)

DecisionTreeClassifier(max_depth=2)

y_pred = clf.predict(X_test)
y_pred

array([1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
       0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0,
       1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1,
       0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1,
       1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1,
       0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1,
       0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1,
       1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1], dtype=int64)

from sklearn import metrics 
Accuracy = metrics.accuracy_score(y_test,y_pred)
Accuracy

0.7

target = [0,1]
print(classification_report(y_test, y_pred))

              precision    recall  f1-score   support

           0       0.46      0.36      0.41        85
           1       0.77      0.83      0.80       215

    accuracy                           0.70       300
   macro avg       0.62      0.60      0.60       300
weighted avg       0.68      0.70      0.69       300

#Let's use Gride dearch to get best parameter and later check accuracy and classification report

grid={'criterion':['gini','entropy'],
                  'max_depth':[90,100,110],
     'min_samples_split':[8,10,12]}

clf_2=DecisionTreeClassifier()
tree_cv=GridSearchCV(clf_2,grid,cv=10,n_jobs=5,verbose=10)

tree_cv.fit(X_train,y_train)

print("tuned hyperparameters :(best parameters) ",tree_cv.best_params_)
print("accuracy :",tree_cv.best_score_)

Fitting 10 folds for each of 18 candidates, totalling 180 fits

[Parallel(n_jobs=5)]: Using backend LokyBackend with 5 concurrent workers.
[Parallel(n_jobs=5)]: Done   3 tasks      | elapsed:    1.9s
[Parallel(n_jobs=5)]: Done   8 tasks      | elapsed:    2.0s
[Parallel(n_jobs=5)]: Done  15 tasks      | elapsed:    2.0s
[Parallel(n_jobs=5)]: Done  22 tasks      | elapsed:    2.0s
[Parallel(n_jobs=5)]: Batch computation too fast (0.1641s.) Setting batch_size=2.
[Parallel(n_jobs=5)]: Done  31 tasks      | elapsed:    2.0s
[Parallel(n_jobs=5)]: Batch computation too fast (0.0247s.) Setting batch_size=4.
[Parallel(n_jobs=5)]: Done  45 tasks      | elapsed:    2.0s
[Parallel(n_jobs=5)]: Batch computation too fast (0.0440s.) Setting batch_size=8.
[Parallel(n_jobs=5)]: Done  79 tasks      | elapsed:    2.1s
[Parallel(n_jobs=5)]: Batch computation too fast (0.1080s.) Setting batch_size=16.
[Parallel(n_jobs=5)]: Done 152 out of 180 | elapsed:    2.3s remaining:    0.3s
[Parallel(n_jobs=5)]: Done 180 out of 180 | elapsed:    2.3s finished

tuned hyperparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 90, 'min_samples_split': 12}
accuracy : 0.7285714285714286

clf_2.fit(X_train,y_train)
y_pred = clf_2.predict(X_test)
y_pred

array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1,
       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0,
       0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1,
       1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0,
       1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,
       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1,
       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0,
       1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1,
       0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0,
       1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1,
       1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1,
       1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1,
       0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0], dtype=int64)

Accuracy = metrics.accuracy_score(y_test,y_pred)
Accuracy

0.66

target = [0,1]
print(classification_report(y_test, y_pred))

              precision    recall  f1-score   support

           0       0.40      0.42      0.41        85
           1       0.77      0.75      0.76       215

    accuracy                           0.66       300
   macro avg       0.59      0.59      0.59       300
weighted avg       0.66      0.66      0.66       300

#! pip install dtreeviz

import sys
import os
# add library module to PYTHONPATH
sys.path.append(f"{os.getcwd()}/../")

from sklearn.datasets import *
from dtreeviz.trees import *
from IPython.display import Image, display_svg, SVG

#import os
#os.environ["PATH"] += os.pathsep + r'C:\Users\deepa\anaconda3\pkgs\graphviz-2.38-hfd603c8_2\Library\bin'

viz = dtreeviz(clf_2,
               X_train,
               y_train,
               target_name='Class',  # this name will be displayed at the leaf node
               feature_names=X.columns,
               title="German Credit data set regression",
               fontname="Arial",
               title_fontsize=16,
               colors = {"title":"purple"}
              )
viz

# Random Forests

from sklearn.ensemble import RandomForestClassifier

# Your code here! :)
clf = RandomForestClassifier(n_estimators=10,random_state=0)

import numpy as np
import matplotlib.pyplot as plt

from sklearn.datasets import make_classification
from sklearn.ensemble import ExtraTreesClassifier

clf.fit(X_train, y_train)

RandomForestClassifier(n_estimators=10, random_state=0)

y_pred = clf.predict(X_test)
y_pred

array([1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1,
       0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0,
       1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1,
       1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,
       1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1,
       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1,
       1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0,
       1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1,
       1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1,
       0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1,
       0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0], dtype=int64)

target = [0,1]
print(classification_report(y_test, y_pred))

              precision    recall  f1-score   support

           0       0.51      0.47      0.49        85
           1       0.80      0.82      0.81       215

    accuracy                           0.72       300
   macro avg       0.66      0.65      0.65       300
weighted avg       0.72      0.72      0.72       300

plt.rc("figure", figsize=(16,8))
plt.rc("font", size=14)

clf.fit(X_train, y_train)
Accuracy=clf.score(X, y)
print('Accuracy:',Accuracy,'\n')

importFeature = clf.feature_importances_
feature_importances=pd.DataFrame([importFeature])

std = np.std([tree.feature_importances_ for tree in clf.estimators_],axis=0)
indices = np.argsort(importFeature)[::-1]

# Print the feature ranking
print("Feature ranking:")

# Plot the feature importances of the forest
plt.figure()
plt.title("Feature importances")
plt.bar(range(X.shape[1]), importFeature[indices],color="r", yerr=std[indices], align="center")
plt.xticks(range(X.shape[1]), indices)
plt.xlim([-1, X.shape[1]])
plt.show()

feature_importances=pd.DataFrame(pd.Series(colNames)[indices])
feature_importances['importance']=np.sort(importFeature)[::-1]
feature_importances.columns=['features','importance']
feature_importances

Accuracy: 0.911 

Feature ranking:

	features	importance
1	Amount	0.104739
0	Duration	0.085347
4	Age	0.077534
12	CheckingAccountStatus.gt.200	0.052844
3	ResidenceDuration	0.033434
...	...	...
57	Housing.ForFree	0.001213
26	Purpose.Vacation	0.000863
22	Purpose.Radio.Television	0.000341
43	Personal.Male.Married.Widowed	0.000000
25	Purpose.Education	0.000000

61 rows × 2 columns